Detection of Subsurface Corrosion Beneath Coatings Using Thermal Time-Series Analysis

Abstract Detecting corrosion concealed beneath protective coatings remains a significant challenge for nondestructive evaluation of assets. In this study, The authors introduce a thermal imaging–based approach that applies transient heating to coated steel specimens and analyzes the resulting surface response using pixel-wise curve fitting and statistical clustering. A thermal infrared camera captured thermal image sequences during controlled halogen lamp excitation. Each pixel’s temperature–time series was fit with a sigmoid model, producing parameter maps that quantified amplitude, slope, and inflection time of the thermal response. Rust-affected areas exhibited delayed but amplified heating response profiles, whereas intact coated regions displayed more moderate responses. Gaussian Mixture Model (GMM) clustering of the fitted parameters successfully separated rust from non-rust regions, providing automated detection and classification without prior labeling. The results demonstrate that transient thermography, combined with sigmoid fitting and clustering, can effectively reveal corrosion hidden beneath coatings, offering a nondestructive, non-contact, and uncertainty-aware tool for integrity assessment of coated metallic surfaces.